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<p>In this work, the application of machine learning (ML) models for the prediction of titanium alloy for electrical discharge machining (EDM) was studied. The machining performances (MRR, TWR, Ra , and recast layer thickness) were predicted by SVM, XG Boost and KNN machine learning models. The heatmap highlighted a strong correlation between parameters and a positive effect was observed for MRR (0.96) and Ra-recast layer thickness (0.82). The performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) were also utilized to compare the models. SVM achieved the best prediction results for TWR and recast layer thickness, while KNN produced the lowest MAE on MRR. The XG Boost model had slightly compromised prediction accuracy than the support vector machine for TWR. These results suggested that a hybridized approach could improve prediction accuracy for complex machining parameters, especially in high-precision machining applications such as biomedical and aerospace industries.</p>

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Exploring SVM, XG Boost, and KNN Machine Learning models for machining Performance of EDM In Titanium Alloys for Accurate Prediction

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Pappu Kumar,
  • Nikhil Kumar Mahraur,
  • Ravi Ranjan Kumar,
  • Chandra Shekhar Verma,
  • Sujeet Kumar

摘要

In this work, the application of machine learning (ML) models for the prediction of titanium alloy for electrical discharge machining (EDM) was studied. The machining performances (MRR, TWR, Ra , and recast layer thickness) were predicted by SVM, XG Boost and KNN machine learning models. The heatmap highlighted a strong correlation between parameters and a positive effect was observed for MRR (0.96) and Ra-recast layer thickness (0.82). The performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) were also utilized to compare the models. SVM achieved the best prediction results for TWR and recast layer thickness, while KNN produced the lowest MAE on MRR. The XG Boost model had slightly compromised prediction accuracy than the support vector machine for TWR. These results suggested that a hybridized approach could improve prediction accuracy for complex machining parameters, especially in high-precision machining applications such as biomedical and aerospace industries.